In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust s...In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.展开更多
借鉴Franses and Ghijsel[1](1999)和Charles and Darne[2](2005)提出的鉴别和校正金融序列加性异常值的方法,以GARCH模型为例,对我国的上证综合指数和深圳成分指数进行了加性异常值的鉴定与校正,并对校正后的残差进行了正态检验。结果...借鉴Franses and Ghijsel[1](1999)和Charles and Darne[2](2005)提出的鉴别和校正金融序列加性异常值的方法,以GARCH模型为例,对我国的上证综合指数和深圳成分指数进行了加性异常值的鉴定与校正,并对校正后的残差进行了正态检验。结果表明该方法效果显著,进行异常值校正后的GARCH(1,1),更好地拟合金融时间序列中的尖峰厚尾和波动丛聚性的特性,纠正了正态分布的GARCH(1,1)对时间序列拟合的偏误。展开更多
文摘In this paper, methods based on ranks and signs for estimating the parameters of thefirst-order integer-valued autoregressive model in the presence of additive outliers are proposed. In particular, we use the robust sample autocorrelations based on ranks and signsto obtain estimators for the parameters of the Poisson INAR(1) process. The effects ofadditive outliers on the estimates of parameters of integer-valued time series are examined. Some numerical results of the estimators are presented with a discussion of theobtained results. The proposed methods are applied to a dataset concerning the numberof different IP addresses accessing the server of the pages of the Department of Statistics of the University of Würzburg. The results presented here give motivation to use themethodology in practical situations in which Poisson INAR(1) process contains additiveoutliers.
文摘借鉴Franses and Ghijsel[1](1999)和Charles and Darne[2](2005)提出的鉴别和校正金融序列加性异常值的方法,以GARCH模型为例,对我国的上证综合指数和深圳成分指数进行了加性异常值的鉴定与校正,并对校正后的残差进行了正态检验。结果表明该方法效果显著,进行异常值校正后的GARCH(1,1),更好地拟合金融时间序列中的尖峰厚尾和波动丛聚性的特性,纠正了正态分布的GARCH(1,1)对时间序列拟合的偏误。
